An Active Dry-Contact Continuous EEG Monitoring System for Seizure Detection Applications in Clinical Neurophysiology
Signals from the proposed system exhibited a correlation coefficient exceeding 0.8 with those from the commercial device. Signal-to-noise ratio analysis indicated noise mitigation performance comparable to the commercial system. The deep learning model achieved accuracy and recall improvements of 2.76% and 16.33%, respectively, over state-of-the-art approaches. The artifact removal algorithm effectively identified and eliminated noise while preserving seizure-related EEG features.
Authors
- Chamira U. S. Edussooriya (ORCID: https://orcid.org/0000-0001-6715-6198)
- Jithangi Wanigasinghe (ORCID: https://orcid.org/0000-0002-9413-8363)
- Nima L. Wickramasinghe (ORCID: https://orcid.org/0000-0001-8133-646X)
- Kithmin Wickremasinghe (ORCID: https://orcid.org/0000-0003-1822-649X)
- Anjula De Silva (ORCID: https://orcid.org/0000-0001-8685-2076)
- Kavindu Weerasinghe
- Dinuka Sandun Udayantha
- Akila Abeyratne
Institutions
- University of British Columbia (CA)
- University of Moratuwa (LK)
- University of Colombo (LK)
Publication Details
- Journal
- IEEE Transactions on Biomedical Engineering
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/tbme.2025.3629563
- Citations
- 1
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 1.43